Consensus-based Recursive Multi-Output Gaussian Process

• URL: http://arxiv.org/abs/2604.10146v1
• Date: Sat, 11 Apr 2026 10:21:03 GMT
• Title: Consensus-based Recursive Multi-Output Gaussian Process
• Abstract summary: Multi-output Gaussian Processes provide principled uncertainty-aware learning of vector-valued fields.<n>This paper proposes a Consensus-based Recursive Multi-Output Gaussian Process framework.<n> Experiments on synthetic wind fields and real LiDAR data demonstrate that CRMGP achieves competitive predictive performance and reliable uncertainty calibration.
• Score: 4.460583138505673
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multi-output Gaussian Processes provide principled uncertainty-aware learning of vector-valued fields but are difficult to deploy in large-scale, distributed, and streaming settings due to their computational and centralized nature. This paper proposes a Consensus-based Recursive Multi-Output Gaussian Process (CRMGP) framework that combines recursive inference on shared basis vectors with neighbour-to-neighbour information-consensus updates. The resulting method supports parallel, fully distributed learning with bounded per-step computation while preserving inter-output correlations and calibrated uncertainty. Experiments on synthetic wind fields and real LiDAR data demonstrate that CRMGP achieves competitive predictive performance and reliable uncertainty calibration, offering a scalable alternative to centralized Gaussian process models for multi-agent sensing applications.

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